Satellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure. In this study, we propose a cross-cloud system for ML-based satellite image detection, focusing on the financial and performance aspects of utilizing Amazon Web Service (AWS) Lambda and Amazon SageMaker for advanced machine learning tasks. Our system utilizes Google Apps Script (GAS) to create a web-based control panel, providing users with access to our AWS-hosted satellite detection models. Additionally, we utilize AWS to manage expenses through a strategic combination of Google Cloud and AWS, providing not only economic advantages, but also enhanced resilience. Furthermore, our approach capitalizes on the synergistic capabilities of AWS and Google Cloud to fortify our defenses against data loss and ensure operational resilience. Our goal is to demonstrate the effectiveness of a cloud environment in addressing complex and interdisciplinary challenges, particularly in the field of object analysis using spatial imagery.

This paper introduces a novel approach for encoding information in PDF documents or similar files. The proposed encoding involves a dual-step method: firstly, the information is encoded in base64, and subsequently, it is uploaded in a user-selected color, while the rest of the colors contain dummy information. Merging of the encoded segments results in a single QR code. The Literature Review subsection investigates the usage of similar methods for information encoding, followed by a comparison of the luminance of the generated QR code with theoretical expectations. Finally, diverse use cases are presented.

We introduce a novel color QR code system for advanced encryption, leveraging spatial data, optimized on AWS. Outperforms conventional systems in speed and cost. Future applications in secure healthcare data storage and quantum color exploration.

This study integrates colorimetry and computation by identifying their commonalities to develop a novel encryption system centered around color, specifically using QR codes. We propose an approach that multiplexes QR codes of varying colors, each containing distinct information. A key is generated to encapsulate user-specific data and identify the QR code with authentic information. We develop serverless architectures to facilitate rapid encryption and decryption processes. The system’s performance and efficiency are evaluated through two architectures: a sequential system implemented on Google Colab and a distributed system utilizing AWS Lambda serverless architecture. Metrics such as NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity) and key space analysis, indicative of the system’s robustness, are analyzed according to existing literature. In addition, the cost of this serverless technology is evaluated in comparison to cloud and local. Our findings demonstrate that the serverless architecture offers a viable and efficient solution for coding. The implications of this research extend across various sectors, including defense, healthcare, and everyday digital interactions, presenting a scalable and secure alternative for data encryption and communication.